Enormous computational load is one of the problems in modern signal processing applications. In order to solve the computational load problem, hardware organization techniques for exploiting parallelism have been developed. Parallel computing is performed by utilizing multiple concurrent processes in the fulfillment of a common task. These processes may be executed on different processors of a multi-processor computer in parallel. Massive parallel processors may even employ thousands of processors which are connected by very fast interconnection networks.
The least-mean square (LMS) algorithm is a linear adaptive filtering algorithm that consists of two basic processes: 1) a filtering process, which involves computing the output of a transversal filter by a set of tap inputs and generating an estimation error by comparing this output to a desired response; and 2) an adaptive process, which involves an automatic adjustment of the tap weights of the filter in accordance with the estimation error. FIG. 3 shows an adaptive filter in which the LSM algorithm has been implemented on parallel processors. However, this introduces a problem in that the rest of operations should be done sequentially.